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Hefei University of Technology (合肥工业大学) 2021

Study on Dynamic Monitoring Model of Drought in Anhui Province Based on Remote Sensing

王军

Titre : Study on Dynamic Monitoring Model of Drought in Anhui Province Based on Remote Sensing

Auteur : 王军

Grade : Master 2021

Université : Hefei University of Technology (合肥工业大学)

Résumé partiel
Drought is one of the major natural disasters facing the world.It has the main characteristics of long duration,wide range of influence and serious disaster losses.The traditional drought monitoring mainly uses the meteorological data of the ground stations to calculate the drought index by spatial interpolation,and obtains the spatial drought distribution map in the region.The traditional drought monitoring method has high precision in a small range,but it is only suitable for small-scale drought monitoring.Traditional meteorological drought monitoring is faced with many problems such as large number of stations,small coverage and large workload,which makes it difficult to realize large-scale dynamic drought monitoring.With the rapid development of remote sensing technology,it provides a large-scale dynamic monitoring method for drought monitoring,and has a good effect on drought monitoring.The state index and the distance index can reflect the drought of vegetation to some extent.Multi source data can synthesize the information of drought factors of multiple planting.A lot of research has proved that machine learning method has a strong data mining ability.Mining remote sensing data based on machine learning method will improve the accuracy of drought monitoring.In order to monitor drought more accurately,based on the detailed description of the scientific research progress of drought remote sensing monitoring at home and abroad,the paper discusses the research progress of various machine learning methods and drought remote sensing monitoring models.

Mots clés : drought remote sensing monitoring model ;three element connection number subtraction set pair potential ;data fusion ;anomaly classification regression tree model ;anomaly multiple linear regression model ;

Présentation (CNKI)

Page publiée le 8 mars 2022